Among the most widely farmed and eaten crops globally are corn, rice, and bananas. However, a variety of both biological and environmental variables, including weather, pests, soil, and water quality, can influence these crops, leading to illnesses that have a substantial impact on crop quality and output. These illnesses not only cause financial loss but also represent a serious danger to the farming sector. Traditional methods of detecting diseases are insufficient and frequently depend on laborious eye examinations. Consequently, having efficient techniques for identifying and treating these illnesses is crucial. Using a Deep Convolutional Neural Network for the crops of corn, rice, and bananas, this article tackles this problem. A layout in which max-pooling layers are arranged in a deliberate manner after convolutional layers with progressively larger filters to guarantee the identification of certain illness patterns. The findings showed that the accuracy was 99.7% for Bananas, 98.8% for Rice, and 99.8% for Corn. This study aims to empower farmers worldwide and support them in protecting the health and productivity of their crops by showcasing the application of convolutional neural networks in crop disease identification.